PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation
This work addresses the challenge of enabling robots to perform manipulation tasks based on natural language instructions, which is an incremental improvement over existing methods by using 3D point clouds for better accuracy and efficiency.
The paper tackles the problem of language-guided robotic manipulation by proposing PolarNet, a 3D point cloud-based policy that addresses limitations of 2D image representations, such as difficulty in multi-view integration and precise 3D inference. It outperforms state-of-the-art 2D and 3D methods on the RLBench benchmark and shows promising results on a real robot.
The ability for robots to comprehend and execute manipulation tasks based on natural language instructions is a long-term goal in robotics. The dominant approaches for language-guided manipulation use 2D image representations, which face difficulties in combining multi-view cameras and inferring precise 3D positions and relationships. To address these limitations, we propose a 3D point cloud based policy called PolarNet for language-guided manipulation. It leverages carefully designed point cloud inputs, efficient point cloud encoders, and multimodal transformers to learn 3D point cloud representations and integrate them with language instructions for action prediction. PolarNet is shown to be effective and data efficient in a variety of experiments conducted on the RLBench benchmark. It outperforms state-of-the-art 2D and 3D approaches in both single-task and multi-task learning. It also achieves promising results on a real robot.